{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import cv2 as cv\n",
    "from sklearn import svm, datasets\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import classification_report\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>怀孕次数</th>\n",
       "      <th>口服葡萄糖耐量试验中血浆葡萄糖浓度</th>\n",
       "      <th>舒张压(mm Hg)</th>\n",
       "      <th>三头肌组织褶厚度(mm)</th>\n",
       "      <th>2小时血清胰岛素(μU/ ml)</th>\n",
       "      <th>体重指数（kg/（身高(m)）^ 2）</th>\n",
       "      <th>糖尿病系统功能</th>\n",
       "      <th>年龄</th>\n",
       "      <th>是否患病</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   怀孕次数  口服葡萄糖耐量试验中血浆葡萄糖浓度  舒张压(mm Hg)  三头肌组织褶厚度(mm)  2小时血清胰岛素(μU/ ml)  \\\n",
       "0     6                148          72            35                 0   \n",
       "1     1                 85          66            29                 0   \n",
       "2     8                183          64             0                 0   \n",
       "3     1                 89          66            23                94   \n",
       "4     0                137          40            35               168   \n",
       "\n",
       "   体重指数（kg/（身高(m)）^ 2）   糖尿病系统功能  年龄  是否患病  \n",
       "0                  33.6    0.627  50     1  \n",
       "1                  26.6    0.351  31     0  \n",
       "2                  23.3    0.672  32     1  \n",
       "3                  28.1    0.167  21     0  \n",
       "4                  43.1    2.288  33     1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('C:/Users/admin/Desktop/csdn作业/pima-indians-diabetes.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "怀孕次数                    768 non-null int64\n",
      "口服葡萄糖耐量试验中血浆葡萄糖浓度       768 non-null int64\n",
      "舒张压(mm Hg)              768 non-null int64\n",
      "三头肌组织褶厚度(mm)            768 non-null int64\n",
      "2小时血清胰岛素(μU/ ml)        768 non-null int64\n",
      "体重指数（kg/（身高(m)）^ 2）     768 non-null float64\n",
      "糖尿病系统功能                 768 non-null float64\n",
      "年龄                      768 non-null int64\n",
      "是否患病                    768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a0eec1da58>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df.怀孕次数)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols=df.columns\n",
    "feat_corr=df.corr().abs()\n",
    "plt.subplots(figsize=(13,9))\n",
    "sns.heatmap(feat_corr,annot=True,)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>怀孕次数_log</th>\n",
       "      <th>口服葡萄糖耐量试验中血浆葡萄糖浓度_log</th>\n",
       "      <th>舒张压(mm Hg)_log</th>\n",
       "      <th>三头肌组织褶厚度(mm)_log</th>\n",
       "      <th>2小时血清胰岛素(μU/ ml)_log</th>\n",
       "      <th>体重指数（kg/（身高(m)）^ 2） _log</th>\n",
       "      <th>糖尿病系统功能_log</th>\n",
       "      <th>年龄_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.945910</td>\n",
       "      <td>5.003946</td>\n",
       "      <td>4.290459</td>\n",
       "      <td>3.583519</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.543854</td>\n",
       "      <td>0.486738</td>\n",
       "      <td>3.931826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.693147</td>\n",
       "      <td>4.454347</td>\n",
       "      <td>4.204693</td>\n",
       "      <td>3.401197</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.317816</td>\n",
       "      <td>0.300845</td>\n",
       "      <td>3.465736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.197225</td>\n",
       "      <td>5.214936</td>\n",
       "      <td>4.174387</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.190476</td>\n",
       "      <td>0.514021</td>\n",
       "      <td>3.496508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.693147</td>\n",
       "      <td>4.499810</td>\n",
       "      <td>4.204693</td>\n",
       "      <td>3.178054</td>\n",
       "      <td>4.553877</td>\n",
       "      <td>3.370738</td>\n",
       "      <td>0.154436</td>\n",
       "      <td>3.091042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.927254</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>3.583519</td>\n",
       "      <td>5.129899</td>\n",
       "      <td>3.786460</td>\n",
       "      <td>1.190279</td>\n",
       "      <td>3.526361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   怀孕次数_log  口服葡萄糖耐量试验中血浆葡萄糖浓度_log  舒张压(mm Hg)_log  三头肌组织褶厚度(mm)_log  \\\n",
       "0  1.945910               5.003946        4.290459          3.583519   \n",
       "1  0.693147               4.454347        4.204693          3.401197   \n",
       "2  2.197225               5.214936        4.174387          0.000000   \n",
       "3  0.693147               4.499810        4.204693          3.178054   \n",
       "4  0.000000               4.927254        3.713572          3.583519   \n",
       "\n",
       "   2小时血清胰岛素(μU/ ml)_log  体重指数（kg/（身高(m)）^ 2） _log  糖尿病系统功能_log    年龄_log  \n",
       "0              0.000000                  3.543854     0.486738  3.931826  \n",
       "1              0.000000                  3.317816     0.300845  3.465736  \n",
       "2              0.000000                  3.190476     0.514021  3.496508  \n",
       "3              4.553877                  3.370738     0.154436  3.091042  \n",
       "4              5.129899                  3.786460     1.190279  3.526361  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df['是否患病']\n",
    "_X=df.drop('是否患病',axis=1)\n",
    "_X=np.log1p(_X)\n",
    "cate=_X.columns+'_log'\n",
    "X=pd.DataFrame(columns=cate,data=_X.values)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "ms_log=MinMaxScaler()\n",
    "feat_names_log=X.columns\n",
    "X_train_log = ms_log.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>怀孕次数_log</th>\n",
       "      <th>口服葡萄糖耐量试验中血浆葡萄糖浓度_log</th>\n",
       "      <th>舒张压(mm Hg)_log</th>\n",
       "      <th>三头肌组织褶厚度(mm)_log</th>\n",
       "      <th>2小时血清胰岛素(μU/ ml)_log</th>\n",
       "      <th>体重指数（kg/（身高(m)）^ 2） _log</th>\n",
       "      <th>糖尿病系统功能_log</th>\n",
       "      <th>年龄_log</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.673239</td>\n",
       "      <td>0.944441</td>\n",
       "      <td>0.891583</td>\n",
       "      <td>0.778151</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.839581</td>\n",
       "      <td>0.356534</td>\n",
       "      <td>0.639050</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.239812</td>\n",
       "      <td>0.840710</td>\n",
       "      <td>0.873760</td>\n",
       "      <td>0.738561</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.786030</td>\n",
       "      <td>0.195523</td>\n",
       "      <td>0.284791</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.760188</td>\n",
       "      <td>0.984263</td>\n",
       "      <td>0.867462</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.755862</td>\n",
       "      <td>0.380165</td>\n",
       "      <td>0.308180</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.239812</td>\n",
       "      <td>0.849290</td>\n",
       "      <td>0.873760</td>\n",
       "      <td>0.690106</td>\n",
       "      <td>0.675479</td>\n",
       "      <td>0.798568</td>\n",
       "      <td>0.068711</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.929966</td>\n",
       "      <td>0.771702</td>\n",
       "      <td>0.778151</td>\n",
       "      <td>0.760921</td>\n",
       "      <td>0.897058</td>\n",
       "      <td>0.965907</td>\n",
       "      <td>0.330870</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   怀孕次数_log  口服葡萄糖耐量试验中血浆葡萄糖浓度_log  舒张压(mm Hg)_log  三头肌组织褶厚度(mm)_log  \\\n",
       "0  0.673239               0.944441        0.891583          0.778151   \n",
       "1  0.239812               0.840710        0.873760          0.738561   \n",
       "2  0.760188               0.984263        0.867462          0.000000   \n",
       "3  0.239812               0.849290        0.873760          0.690106   \n",
       "4  0.000000               0.929966        0.771702          0.778151   \n",
       "\n",
       "   2小时血清胰岛素(μU/ ml)_log  体重指数（kg/（身高(m)）^ 2） _log  糖尿病系统功能_log    年龄_log  \\\n",
       "0              0.000000                  0.839581     0.356534  0.639050   \n",
       "1              0.000000                  0.786030     0.195523  0.284791   \n",
       "2              0.000000                  0.755862     0.380165  0.308180   \n",
       "3              0.675479                  0.798568     0.068711  0.000000   \n",
       "4              0.760921                  0.897058     0.965907  0.330870   \n",
       "\n",
       "   Target  \n",
       "0       1  \n",
       "1       0  \n",
       "2       1  \n",
       "3       0  \n",
       "4       1  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = pd.Series(data = y, name = 'Target')\n",
    "train_log = pd.concat([pd.DataFrame(columns = feat_names_log, data = X_train_log), y], axis = 1)\n",
    "train_log.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logloss of each fold is:  [-0.68181818 -0.69480519 -0.66233766 -0.67973856 -0.64052288]\n",
      "cv logloss is: -0.6718444953739071\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "y_train=train_log['Target']\n",
    "X_train=train_log.drop(['Target'],axis=1)\n",
    "feat_names = X_train.columns\n",
    "#默认参数的Logistic Regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr=LogisticRegression()\n",
    "from sklearn.model_selection import cross_val_score\n",
    "loss=cross_val_score(lr,X_train,y_train,cv=5) #5折交叉验证\n",
    "print ('logloss of each fold is: ',-loss)\n",
    "print('cv logloss is:', -loss.mean())\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5444819443182722\n",
      "{'C': 100, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "fold = StratifiedKFold(n_splits=5, shuffle=True, random_state=777)\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression(solver='liblinear')\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=fold, scoring='neg_log_loss',n_jobs = 4,)#用log似然损失\n",
    "grid.fit(X_train,y_train)\n",
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.7604166666666666\n",
      "{'C': 10, 'penalty': 'l1'}\n"
     ]
    }
   ],
   "source": [
    "lr_penalty= LogisticRegression(solver='liblinear')\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=fold, scoring='accuracy',n_jobs = 4,) #正确率\n",
    "grid.fit(X_train,y_train)\n",
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'cPickle'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-25-dc4df994d640>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mcPickle\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mcPickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pima.pkl\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'wb'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'cPickle'"
     ]
    }
   ],
   "source": [
    "import cPickle\n",
    "cPickle.dump(grid.best_estimator_, open(\"pima.pkl\", 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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